spec.dat

spec <- read.csv("tutorial/CF_demographic/input/spec.dat")

datatable(spec,rownames = FALSE, options = list(searching = FALSE))

puvspr.dat

The table shown here is a trimmed version showing the first 30 rows as an example of the type of data in the puvspr.dat file. The original dataset has 974 entries.

puvspr <- read.csv("tutorial/CF_demographic/input/puvspr.dat")

datatable(puvspr[1:30,],rownames = FALSE, options = list(searching = FALSE))

pu.dat

The table shown here is a trimmed version showing the first 30 rows as an example of the type of data in the pu.dat file. The original dataset has 653 entries.

pu <- read.csv("tutorial/CF_demographic/input/pu.dat")

datatable(pu[1:30,],rownames = FALSE, options = list(searching = FALSE))

Initial Conservation Feature

This map shows the bioregions, which serve as conservation features in the Marxan analysis with no connectivity.

puvspr_wide <- puvspr %>%

left_join(select(spec,"id","name"),

by=c("species"="id")) %>%

select(-species) %>%

spread(key="name",value="amount")

# planning units with output

output <- read.csv("tutorial/CF_demographic/output/pu_no_connect.csv") %>%

mutate(geometry=st_as_sfc(geometry,"+proj=longlat +datum=WGS84"),

best_solution = as.logical(best_solution)) %>%

st_as_sf() %>%

left_join(puvspr_wide,by=c("FID"="pu"))

map <- leaflet(output) %>%

addTiles()

groups <- names(select(output,-best_solution,-select_freq, -google_demo_pu))[c(-1,-2)]

groups <- groups[groups!="geometry"]

for(i in groups){

z <- unlist(data.frame(output)[i])

if(is.numeric(z)){

pal <- colorBin("YlOrRd", domain = z)

}else{

pal <- colorFactor("YlOrRd", domain = z)

}

map = map %>%

addPolygons(fillColor = ~pal(z),

fillOpacity = 0.6,

weight=0.5,

color="white",

group=i,

label = as.character(z)) %>%

addLegend(pal = pal,

values = z,

title = i,

group = i,

position="bottomleft")

}

map <- map %>%

addLayersControl(overlayGroups  = groups,

options = layersControlOptions(collapsed = FALSE))

for(i in groups){

map <- map %>% hideGroup(i)

}

map %>%

showGroup("BIORE_102")

Connectivity data

Now let’s examine the spatial layers we’ve added in order to incorporate connectivity into the Marxan analysis. Marxan Connect needs a shapefile for the planning units, the focus areas, and the avoidance areas. These spatial layers are shown in the map below.

# planning units

pu <- st_read("tutorial/CF_demographic/hex_planning_units.shp") %>%

st_transform(proj)

#focus areas (IUCN level I or II protected areas)

fa <- st_read("tutorial/CF_demographic/IUCN_IorII.shp") %>%

st_transform(proj)

# avoidance areas (ports)

aa <- st_read("tutorial/CF_demographic/ports.shp") %>%

st_transform(proj)

p <- qtm(pu,fill = '#7570b3') +

qtm(fa,fill = '#1b9e77') +

qtm(aa,fill = '#d95f02')

tmap_leaflet(p) %>%

addLegend(position = "topright",

labels = c("Planning Units","Focus Areas (IUCN I or II)","Avoidance Areas (ports)"),

colors = c("#7570b3","#1b9e77","#d95f02"),

title = "Layers")

connectivity_matrix.csv

The connectivity data is at the ‘heart’ of Marxan Connect’s functionality. It allows the generation of new conservation features based on connectivity metrics.

The connectivity data for this analysis can be found in the hexFlow.csv file in the CF_demographic folder. For this example we have created the connectivity matrix for you. However, for demographic connectivity analyses, this file is user created from demographic data, tagging, etc. For landscape connectivity data, Marxan Connect does have some functionality to create connectivity matrices from resistance data (see landscape connectivity tutorial (./CSD_landscape.html)).

For the sake of your web browser, this table only contains the 7 row and columns of the connectivity matrix. The real file has 653 X 653.

conmat <- read.csv("tutorial/CF_demographic/hexFlow.csv",check.names = FALSE)[1:7,1:7]

datatable(conmat,rownames = FALSE, options = list(searching = FALSE))

Loading your project in Marxan Connect

Now that we have explored the input files, we are ready to open Marxan Connect and load our project. Please load the tutorial.MarCon file in the CF_demographic folder into Marxan Connect. You should not have to change any inputs in the project, but it is important to understand what these choices entail.

Figure 1: Loading Marxan Connect tutorial .

We will now step through the Marxan Connect workflow following the work flow tabs.

Figure 2: Workflow tabs .

Spatial Input

After loading tutorial.MarCon your Spatial Input tab should now look something like this:

Figure 3: Spatial Input tab .

Make sure that the Planning Unit Shapefile, Focus Area Shapefile and Avoidance Area Shapefile correspond to the appropriate input files. On this tab, you can also choose whether the Focus Area Shapefile and Avoidance Areas Shapefile should be locked in, locked out, or status-quo (i.e. the planning unit status stays the same as what is designated in the pu.dat file). In this example, our Focus Areas represents protected areas with IUCN categories I and II, and our Avoidance Areas represent ports. We have chosen to keep the same planning unit “status” (locked in/locked out) as the pu.dat file by selecting the status quo option.

Now we are ready to proceed to the Connectivity Input tab.

Connectivity Input

Since we are working with demographic data, we choose the Demographic Input option from the Choose Connectivity Input Category dropdown menu. Alternatively, Landscape Input could be chosen (see landscape connectivity tutorial (./CSD_landscape.html)).

On this tab, ensure that the Connectivity Matrix refers to the correct file. For this example, this should read hexFlow.csv. Since this data represents the amount of movement between planning units (i.e. number of larvae) in a matrix format (i.e. a flow matrix), we have selected the Flow Connectivity Matrix Type and the Matrix Format.

Additionally, we have chosen the Identical Grids option from the Rescale Connectivity Matrix menu. This signifies that our connectivity data is on an identical grid as our planning units. If our connectivity data was not on the same grid as our planning units, we could chose to rescale the connectivity matrix, which essentially does a spatial averaging routine to calculate connectivity on the planning unit grid. If rescaling is chosen, the user needs to decide whether to rescale the connectivity data proportional to the overlap or to assume homogeneous connectivity. For example, if a planning unit has a 50% overlap with connectivity data (i.e. half of the planning unit has connectivity data, and the other half does not), and the connectivity value is 10, does the user want the connectivity value to be taken from a spatial average across that planning unit (i.e. the connectivity value of that planning unit would then be 5) or be considered homogenous across the planning unit (i.e. a connectivity value of 10).

Your Connectivity Input tab should look like this (you should not have to change any settings):

Figure 4: Connectivity Input Tab .

Now proceed to the Connectivity Metrics tab.

Connectivity Metrics

In this example, we have chosen to use the google page rank metric as a Conservation Feature to incorporate connectivity, but there are a variety of options to choose from. The drop down menu in the top right corner gives definitions, possible objectives and the mathematical formulations for different conservation feature options.

Figure 5: Connectivity Metrics Tab .

Once you have selected a demographic conservation feature press Calculate Metrics and proceed to the Pre-Evaluation tab.

Pre-Evaluation

This page allows you to evaluate the metrics created on the Connectivity Metrics tab in more detail. It also allows you to choose how you would like to transform the continuous connectivity metrics into conservation features for further analysis. To discretize the continuous metric, we’ve chosen the median of the continuous metric Google PageRank as lower threshold and the maximum value as the upper threshold. We are setting a target of 50% for this new discrete conservation feature, which means we are aiming to protect half of the planning units which had a Google PageRank score above the median. In this example, we append the new connectivity based conservation metrics to the existing Marxan files by selecting Append under the Metrics option box.

Figure 6: Pre-Evaluation tab .

Once the desired options have been chosen, press Export Files in the bottom right corner of the screen. This will create two new files (or update the files, as they have already been created) called puvspr_connect.dat and spec_connect.dat in the input folder of the CF_demographic folder.

Let’s examine the appended files.

spec <- read.csv("tutorial/CF_demographic/input/spec_connect.dat")

datatable(spec,rownames = FALSE, options = list(searching = FALSE))

puvspr <- read.csv("tutorial/CF_demographic/input/puvspr_connect.dat")

datatable(puvspr[1:30,],rownames = FALSE, options = list(searching = FALSE))

# planning units with output

output <- read.csv("tutorial/CF_demographic/output/pu_connect.csv") %>%

mutate(geometry=st_as_sfc(geometry,"+proj=longlat +datum=WGS84"),

best_solution = as.logical(best_solution)) %>%

st_as_sf()

map <- leaflet(output) %>%

addTiles()

groups <- c("best_solution","select_freq")

for(i in groups){

z <- unlist(data.frame(output)[i])

if(is.numeric(z)){

pal <- colorBin("YlOrRd", domain = z)

}else{

pal <- colorFactor("YlOrRd", domain = z)

}

map = map %>%

addPolygons(fillColor = ~pal(z),

fillOpacity = 0.6,

weight=0.5,

color="white",

group=i,

label = as.character(z)) %>%

addLegend(pal = pal,

values = z,

title = i,

group = i,

position="bottomleft")

}

map <- map %>%

addLayersControl(overlayGroups  = groups,

options = layersControlOptions(collapsed = FALSE))

for(i in groups){

map <- map %>% hideGroup(i)

}

map %>%

showGroup("select_freq")

# planning units with output

output <- read.csv("tutorial/CF_demographic/output/pu_no_connect.csv") %>%

mutate(geometry=st_as_sfc(geometry,"+proj=longlat +datum=WGS84"),

best_solution = as.logical(best_solution)) %>%

st_as_sf()

map <- leaflet(output) %>%

addTiles()

groups <- c("best_solution","select_freq")

for(i in groups){

z <- unlist(data.frame(output)[i])

if(is.numeric(z)){

pal <- colorBin("YlOrRd", domain = z)

}else{

pal <- colorFactor("YlOrRd", domain = z)

}

map = map %>%

addPolygons(fillColor = ~pal(z),

fillOpacity = 0.6,

weight=0.5,

color="white",

group=i,

label = as.character(z)) %>%

addLegend(pal = pal,

values = z,

title = i,

group = i,

position="bottomleft")

}

map <- map %>%

addLayersControl(overlayGroups  = groups,

options = layersControlOptions(collapsed = FALSE))

for(i in groups){

map <- map %>% hideGroup(i)

}

map %>%

showGroup("select_freq")

Plotting Options

Here the users can plot up to two layers of input or output data on a built in basemap (provided by the basemap package). The tab allows users to choose a few basic color, transparency, and legend positioning options, but this tab also allows users to export the output data in various formats to provide further plotting or analysis options.